东亚夏季风的年际变率是气候预测的重要问题。热带太平洋和印度洋的海洋热状况是预测东亚夏季风变异的重要外强迫因子。而典型El Ni?o事件（暖海温主要出现在热带东太平洋）、El Ni?o Modoki事件（暖海温主要出现在热带中太平洋）和印度洋偶极子（IOD）事件是热带太平洋和印度洋海温年际变化的主要模态。以往的研究或者主要关注热带太平洋增暖事件和IOD事件对东亚夏季风的共同影响，而未将El Ni?o事件进行分类；或者主要关注典型El Ni?o和El Ni?o Modoki对东亚夏季风的不同影响，忽略了印度洋IOD事件在其中所起的作用。基于以上考虑，本研究拟通过资料分析和数值模拟等方法来全面的分析热带太平洋典型El Ni?o、El Ni?o Modoki和印度洋IOD事件对东亚夏季风的综合影响，并揭示其中的机理。本项目对认识和理解东亚夏季气候异常有重要的科学意义。

The prediction of the East Asian summer monsoon (EASM) variability on the interannual timescales is an important problem. Among the factors that influence EASM, the sea surface temperature anomalies (SSTAs) in the tropical Pacific and Indian Ocean are regarded as the most important one. Furthermore, the conventional El Ni?o and El Ni?o Modoki are the two leading mode in the EOF analysis of the monthly mean SSTAs in the tropical Pacific. The conventional El Ni?o is characterized by the dipole SSTAs pattern, with the positive SSTAs over the tropical eastern Pacific and the negative SSTAs over the western Pacific. In contrast, the El Ni?o Modoki is accompanied by tripolar SSTAs pattern, with warming SSTAs over the central Pacific and cooling SSTAs over the western and eastern Pacific. In addition, the Indian Ocean Dipole (IOD) phenomenon is a dominant ocean-atmosphere mode in the Indian Ocean. Therefore, the conventional El Ni?o, El Ni?o Modoki and IOD play a critical role in the interannual variability of the EASM. Previous literature, on the one hand, usually focuses on the combined impacts of El Ni?o and IOD on the EASM, while mixes the conventional El Ni?o and El Ni?o Modoki together. On the other hand, some previous literature classifies the El Ni?o events into convention El Ni?o and El Ni?o Modoki, and then investigates the respective influences of the two types of El Ni?o on the EASM, whereas overlooks the action of IOD in this process. Hence, the objective of this present program is to investigate the combined influences of the two types of the tropical Pacific Ocean warming events and IOD on the EASM and its related mechanism. Making clear this problem is in favor of improving the prediction level of the EASM variation.